Recent Advances in Intelligent-Based Structural Health Monitoring of Civil Structures

Paul, Satyam; Jafari, Raheleh

doi:10.25046/aj030540

Cited by 8 publications

(2 citation statements)

References 100 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the initial damage occurrence can be monitored by using the variation of the structural modal parameters as a damage indicator. [8][9][10][11][12][13][14][15] However, vibration-based structural damage detection methods are divided into: (a) conventional category, which includes the structural damage detection methods that just use the mechanical parameters of the structure itself such as natural frequency, damping ratio, mode shape, etc. These methods mostly require performing modal analysis experimentally or measuring the transfer function.…”

Section: Shm Methodsmentioning

confidence: 99%

“…Usually, for diagnostics are used: Visual diagnostics, Ultrasonic testing, Thermography; X-Ray testing, Eddy current; Electromagnetic Testing, Acoustic emission measurements, measurements and analysis of vibrations. 8,[13][14][15][16][17] Comparing all mentioned methods can be stated that vibration analysis is one of the most universal and commonly used methods of technical diagnostics since it is suitable for diagnostics of…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel structural damage identification method based on the acceleration responses under ambient vibration and an optimized deep residual algorithm

Al-Azzawi

Wang

2022

Structural Health Monitoring

View full text Add to dashboard Cite

Recently, structural health monitoring (SHM) methods for civil structures have been investigated widely, especially Deep Learning (DL)-based methods. However, it is usually difficult to fully train a deep neural network, and thus, typical DL-based SHM methods are limited in terms of performance. While addressing these issues, in this paper, a novel methodology is proposed for smart damage identification of frame structures. The newly proposed SHM method is based on raw time-domain structural response signals and deep residual network (DRN). The introduced DRN algorithm has been designed and tested in an effective way for extracting and learning the optimum features of the 1D raw ambient vibration acceleration signals, without any need for engineered features. Also, the network’s performance has been optimized using Bayesian optimization, which clearly enhances the network’s accuracy and information flow across it. Next, the outputs of DRNs are further utilized through new methods for damage size estimation and damage localization. The proposed methodology has been evaluated using the datasets of numerical and experimental frames of the SHM benchmark problem and the dataset of a real-world full-scale truss bridge. The results show that the proposed method is capable of detecting, localizing, and quantifying structural damage accurately for all of the simulated cases of the two examples. Furthermore, conducted comparison studies have approved that the new approach is more efficient than other machine learning-based methods, and it can overcome the major limitations of Artificial intelligence-based SHM models.

show abstract